Tag: 100 Days Challenge

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Muscles: Your Metabolic Powerhouse

Day 3 of 100 Days Muscle Resistance Workout Challenge

Topic: The role of muscle in burning calories, regulating blood sugar, and preventing metabolic disease.

Learning Material

Muscles aren’t just about movement—they’re one of the most important regulators of your metabolism. Think of them as both the engine that burns fuel and the sponge that soaks up sugar from your bloodstream. Without enough muscle, the body’s energy system runs less efficiently, increasing the risk of weight gain, diabetes, and metabolic disease.

Muscle and Calorie Burning

Muscle tissue burns calories even when you’re sitting still. The more muscle you have, the more energy your body uses to simply stay alive. While a single pound of muscle won’t dramatically boost your metabolism on its own, over time, building and maintaining lean mass helps keep your daily calorie burn steady—making it easier to manage weight.

Muscle and Blood Sugar Control


Muscles are the largest site in the body for glucose uptake. Every time you contract a muscle—whether lifting weights or going for a brisk walk—it helps shuttle sugar out of the bloodstream and into muscle cells for storage or use. A Japanese cohort study of young and middle-aged women found that lower relative skeletal muscle mass was strongly associated with reduced insulin sensitivity and impaired glucose tolerance, even among participants who were neither overweight nor diabetic1. This is why strength training and cardio are powerful tools for preventing type 2 diabetes.

Muscle and Metabolic Health

When muscle mass is low, the body becomes less sensitive to insulin, leading to higher blood sugar and increased fat storage. This can trigger a cascade of health problems, including obesity, diabetes, and heart disease. In contrast, keeping muscles active makes them act like a “shield” against these conditions.

Example / Story

Imagine two people who eat the same meal: one has strong, active muscles, while the other has very little muscle. The first person’s muscles quickly absorb the glucose, keeping blood sugar stable. The second person’s body struggles, leaving sugar in the blood, which eventually gets stored as fat. Same meal, completely different outcomes—all because of muscle.

My Reflection

At first, I thought I hadn’t pushed myself hard enough on Day 1 since I didn’t feel sore right away. But this morning, I woke up with aches in my legs and glutes—clear proof that the exercises were working after all.

When I checked my body composition, I noticed something surprising: my muscle mass is in the “dark green” range, which is high for my age. The real issue isn’t muscle but fat—most of my body fat comes from subcutaneous fat, while my visceral fat is less of a concern.

This shows me I need a balanced strategy: keep strengthening my muscles, while also adding more cardio and adjusting my diet to manage fat more effectively.

Today, I made a small but intentional change by starting breakfast with two eggs and a salad. I know this will support fat loss and help me feel satisfied. Still, I realize I’m not eating enough protein overall, so I’ll need to pay closer attention to my intake. After breakfast, I added some light leg stretches—another small step to keep my body moving and recovering.

Biometric data

Change in Weight from Day 1: 0lb

Skeletal Muscle: 38.8%

Muscle Mass: 95 lb.

Adjustment Ideas

  • Add a short cardio session (e.g., 15–20 minutes of cycling, brisk walking, or jogging) 3–4 times a week to pair with muscle training.
  • Reduce added sugars or refined carbs in one daily meal to help keep blood sugar stable.
  • Incorporate more protein + fiber into snacks to feel full longer and support muscle repair.
Day 2

Notes

  1. Hyeon-Ju Kwon et al., “Skeletal Muscle Mass Is Associated with Insulin Sensitivity and Glucose Tolerance in Japanese Women,” Diabetes Research and Clinical Practice 88, no. 2 (2010): 161–168, https://doi.org/10.1016/j.diabres.2010.02.010 ↩︎

Bibliography

Kwon, Hyeon-Ju, Kiyoshi Sanada, Motoyuki Midorikawa, Takashi Iemitsu, Kazushige Murakami, and Mitsuru Higuchi. “Skeletal Muscle Mass Is Associated with Insulin Sensitivity and Glucose Tolerance in Japanese Women.” Diabetes Research and Clinical Practice 88, no. 2 (2010): 161–168. https://doi.org/10.1016/j.diabres.2010.02.010.

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Muscles: Your Anti-Aging Armor

Day 2 of 100 Days Muscle Resistance Workout Challenge

Topic: How maintaining muscle mass protects against aging, frailty, and loss of independence.

Learning Material

Aging is inevitable, but frailty is not. One of the biggest myths about getting older is that weakness, balance issues, and dependence are simply part of the deal. In reality, much of this decline comes from losing muscle mass—a process called sarcopenia.

A comprehensive review of age-related changes in skeletal muscle reports that muscle mass declines by approximately 3–8 percent per decade after age 30, with a more rapid rate of loss after age 60. This decline is frequently accompanied by increased fat mass, resulting in significant shifts in body composition1.

That’s like your body slowly withdrawing money from your “strength bank account” without permission.

Muscle as Independence Insurance


 Simple tasks like standing up from a chair, climbing stairs, or carrying groceries require leg and core strength. When these fade, daily life becomes harder, and the risk of falls skyrockets. Maintaining muscle is less about vanity and more about staying free to live life on your own terms.

The Frailty Domino Effect

Frailty often starts with one small event—a slip, a minor injury, or a short illness. Without muscle reserves, recovery becomes slower, which leads to less movement, which causes even more muscle loss. This cycle can spiral quickly. Training acts like a brake, slowing or even stopping this domino effect.

The “Use It or Lose It” Principle

Muscle is living tissue. If you challenge it, it adapts and grows stronger. If you neglect it, it shrinks. This is why a few weeks of inactivity can cause noticeable weakness, but even people in their 70s and 80s can build muscle with consistent training. Your body never loses its ability to respond—it’s never “too late.”

Example / Story

A controlled study of older adults aged 65–70 compared resistance training, structured walking, and a control condition over a 16-week period. While both exercise interventions improved functional autonomy and walking ability, resistance training produced the greatest gains in isometric and dynamic strength, leading to superior improvements in daily functional independence2.

My Reflection

I didn’t feel much soreness in my legs yesterday, probably because brisk walking and light jogging are already part of my routine. That tells me I may need to adjust my training to really challenge my muscles. Should I add more weight or increase the number of sets? For now, I’ll keep observing, but if I don’t feel any muscle ache at all, I’ll definitely tweak the reps.

With yesterday’s lesson in mind, I focused on my abdominal muscles today. The routine I created was tough, and I could feel it working my core. I actually enjoy the sensation of muscle soreness—it reassures me that I’ve put in the effort—so I’m hoping I’ll feel some tomorrow.

I also thought about my grandmother. In her 80s, she could still walk 10 kilometers without trouble. It wasn’t because she trained for fitness—she simply stayed active throughout her life. I remember traveling with her, visiting many places, and being amazed at how easily she kept up.

While I was doing cardio, I pictured myself at 80. I want to be able to travel, walk, and see many places just like she did. With my current condition, maybe I could—but only if I keep investing in my health. To make that possible, I need to keep building strength now so that I can preserve my mobility for the future.

Biometric data

Change in Weight from Day 1: 0 lb

Skeletal Muscle: 38.8%

Muscle Mass: 95 lb

Adjustment Ideas

  • Add one new leg-focused exercise to your routine (lunges, wall sits, or step-ups) to push beyond walking/jogging.
  • Try “progressive overload” by slightly increasing reps, sets, or intensity over time to keep muscles adapting.
  • Track which exercises actually make you sore or feel challenged—use this as feedback to refine your routine.

Day 1

Note

  1. William K. Mitchell et al., “Sarcopenia, Dynapenia, and the Impact of Advancing Age on Human Skeletal Muscle Size and Strength; A Quantitative Review,” Frontiers in Physiology 3 (2012): 260, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3401542/ ↩︎
  2. Eduardo L. Cadore and Martim Bottaro, “Strength and Endurance Training Prescription in Healthy and Frail Elderly,” Sports Medicine 44, no. 9 (2014): 1257–1276, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4132023/ ↩︎

Bibliography

Cadore, Eduardo L., and Martim Bottaro. “Strength and Endurance Training Prescription in Healthy and Frail Elderly.” Sports Medicine 44, no. 9 (2014): 1257–1276. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4132023/.

Mitchell, William K., John Williams, Philip Atherton, Melanie Larvin, Jean Lund, and Marco Narici. “Sarcopenia, Dynapenia, and the Impact of Advancing Age on Human Skeletal Muscle Size and Strength; A Quantitative Review.” Frontiers in Physiology 3 (2012): 260. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3401542/

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Muscles: Your Built-in Fountain of Youth

Day 1 of 100 Days Muscle Resistance Workout Challenge

Topic: Why muscles matter: longevity, metabolism, resilience, and brain health.

Learning Material


When most people think about muscles, they imagine strength, big biceps, or toned legs. But muscles are much more than aesthetics—they’re one of the most powerful predictors of long-term health and quality of life.

Longevity

Research shows that people with higher muscle mass live longer, even when accounting for weight and other health factors. A large observational study using NHANES III data found that greater skeletal muscle mass, measured by bioelectrical impedance, was independently associated with reduced all-cause mortality in adults aged 55 and older, even after adjusting for BMI and other health factors1.

Muscles act as a “reserve tank” during illness, giving your body resources to recover faster. Think of it as an emergency savings account: when life throws you a health crisis, muscles give your body the buffer it needs.

Metabolism


Muscles are your body’s metabolic engine. A pound of muscle burns roughly three times more calories at rest than a pound of fat. That may sound small, but it adds up over time. More muscle means a higher resting metabolism, which helps with weight management and stable energy.

Resilience & Brain Health


Stronger muscles don’t just help you lift things—they help you stay independent as you age. Falls are a leading cause of disability in older adults, but strength training reduces that risk dramatically. Even more surprising: muscle contractions release chemicals called myokines, which travel to the brain and support memory, learning, and emotional balance. It’s like giving your brain a workout every time you move.

Example / Story


 Imagine two 70-year-olds: one who maintained regular strength training and one who didn’t. The first can climb stairs, carry groceries, and play with grandchildren. The second struggles with simple movements, becomes dependent on others, and faces a higher risk of dementia. Both people aged the same number of years, but their “muscle choices” shaped entirely different futures.

My Reflection

I used to work in long-term care, where I saw firsthand how often people’s health declined after a fall. Many patients seemed fine one day, then their independence slipped away after an injury. Those experiences stay with me, especially because I know I don’t have enough muscle myself. I want to live healthily well into old age and avoid that same path.

My daily routine has always included cardio and some stretching, but I’ve avoided strength training. It’s not that I dislike exercise; I actually enjoy running, but lifting or resistance work never appealed to me. Recently, though, I’ve noticed a gradual loss of muscle mass. The wake-up call came after I fell during one of my cardio sessions. There were many factors, but the truth is clear: I need to stop this decline before it affects my future independence.

I’ve decided to treat strength training as “future insurance”—an investment so that I can stay active when I’m 80 and beyond. Last night, I created a small routine for my first week. Since I don’t know much about muscle training, I’ll keep it simple at first, then adjust and learn over time. My goal is to build a sustainable system, just like I did with my philosophy and coding projects.

For now, I’ll start with 10 minutes of strength work each day for the next 100 days. It may not be a full program yet, but it’s far better than doing nothing. I should have started long ago—but now is the time.

Biometric data

Change in Weight from Day 1: 0 lb.

Skeletal Muscle: 38.8%

Muscle Mass: 95 lb.

Adjustment Ideas (Strategic adjustment)

  • Add a 5-minute morning stretch with one or two bodyweight exercises (push-ups, squats, or planks).
  • Aim for 10–15 grams of protein at each meal (e.g., Greek yogurt, eggs, edamame, chicken).

Day 0

Note

  1. Arun Srikanthan and Arun S. Karlamangla, “Muscle Mass Index as a Predictor of Longevity in Older Adults,” American Journal of Medicine 127, no. 6 (2014): 547–553, https://doi.org/10.1016/j.amjmed.2014.02.007 ↩︎

Bibliography

Srikanthan, Arun, and Arun S. Karlamangla. “Muscle Mass Index as a Predictor of Longevity in Older Adults.” American Journal of Medicine 127, no. 6 (2014): 547–553. https://doi.org/10.1016/j.amjmed.2014.02.007

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Python Company Email Finder for Hobbyists (Hunter.io Free Tier)

Day 100 of 100 Days Coding Challenge: Python

Guess what? Today marks my 100th consecutive day of coding. A round of applause, a pat on the back, maybe even a slice of cake—yes, I’ll take all of it. But here’s the twist: I’m not stopping. I’ve started a project (Business Rader 3000) that still has at least 11 days to go, and honestly, I’m too hooked to quit now.

For this milestone day, I tackled something spicy: scraping email contacts for companies. My plan was to use Hunter.io and LinkedIn. Before diving in, I checked Hunter.io and—surprise—they actually hand you 50 free credits a month. For my purposes (coding fun, not spamming every marketing lead in the world), that’s more than enough. I even signed up, grabbed my shiny new API key, and tucked it away like treasure. Little prep steps like that may sound boring, but trust me: they’re the difference between cruising through the code and banging your head against the keyboard later.

Today’s Motivation / Challenge

Why does this matter? Because a business radar that doesn’t give you contact info is like a phone book without phone numbers: decorative, but not terribly useful. Adding email search makes the project feel alive—it bridges the gap between “Hey, I know this company exists” and “Here’s a real human you could reach out to.” For a hobbyist project, it’s part detective work, part magic trick.

Purpose of the Code (Object)

The code connects to the Hunter.io API and asks for email addresses related to the company’s domain. If successful, it returns a tidy list of emails, along with names and job titles when available. Think of it as a very polite assistant who goes knocking on digital doors and brings back a calling card.

AI Prompt

Please add:

Day 4 of Business Rader 3000
 Add this function:

  • LinkedIn or Hunter.io Email Search.
  • Use web scraping or Hunter.io API to extract emails.
  • Optionally parse LinkedIn names (only if it’s legally allowed).

Functions & Features

  • Take a company name and resolve it to a domain.
  • Query Hunter.io API for emails connected to that domain.
  • Display results with name, title, and email.
  • Track credit usage so you don’t burn through your free quota.

Requirements / Setup

You’ll need:

  • Python 3.11+
  • Install libraries:

pip install requests

(Plus a Hunter.io API key saved in your .env file.)

Minimal Code Sample

import requests, os

api_key = os.getenv(“HUNTER_API_KEY”)

domain = “ford.com”

url = f”https://api.hunter.io/v2/domain-search?domain={domain}&api_key={api_key}”

resp = requests.get(url).json()

print(resp.get(“data”, {}).get(“emails”, [])[:3])  # show first 3 emails

This snippet grabs a few emails from Hunter.io for Ford—costs you one credit, but gives you real contact info.

business-rader-3000

Notes / Lessons Learned

The process was smoother than I expected, but I did trip over a silly variable mismatch. My function call used industry_hint=…, while the helper file similar.py didn’t know what that was. Cue cryptic errors. Once I tied the function signatures together, everything clicked.

Testing with Ford Motor Company gave me several emails right away, and I checked—just one credit gone. Not bad! For hobby use, the free tier is plenty. But if you’re planning to do serious lead generation, you’ll probably want to pony up for a paid plan. For me, though, the thrill was enough: after 100 days of code, I can officially say my project can find people on the internet. That feels oddly powerful.

Optional Ideas for Expansion

  • Add a filter to only return emails with certain job titles (CEO, Marketing Manager, etc.).
  • Save results into a CSV for later use.
  • Combine Hunter.io with another source (like Wikidata titles) for richer context.

Conclusion

This wasn’t exactly Day 100—it was Day 108. That’s when the Business Radar 3000 project was finally completed.

We live in a surprisingly good era. Not long ago, the barrier to entry for programming felt much higher to me. I was always interested, but it was never a priority. At the time, I told myself it was because I was busy earning degrees and professional designations, or because work had me constantly traveling. Eventually, I had to admit the truth: those were excuses.

I’ve never been fond of “book-only” learning. I’m far more output-oriented. What matters to me isn’t the process itself, but what I can actually build from it. There was no guarantee I would enjoy programming—or that I would produce anything useful at all. But curiosity was enough to start.

To create something truly sophisticated, you still need solid programming skills. That hasn’t changed. But even with modest skills—like mine—you can already build meaningful tools. And that alone changes the equation.

That said, programming comes with responsibilities. A few lessons worth remembering:

  • Protect your information. Never hard-code sensitive data when accessing services through Python.
  • Use a .gitignore file. If you’re pushing code to GitHub, protecting your credentials is non-negotiable.
  • Back up everything. I once broke a project I’d been working on for over two weeks and spent hours repairing the damage. Once is enough to learn that lesson.

AI has the potential to close certain knowledge gaps. Someone with limited programming experience can now create simple but useful tools—like a stock tracker, a book tracker, a civilization timeline app, or a business analysis app.

I was curious about how far AI could push me, especially amid constant headlines about programmers losing their jobs and predictions that AI will replace most forms of work. Some displacement may be inevitable. But it’s worth remembering that there are still things AI cannot do.

There are warnings that AI will leave us intellectually hollow, disengaged from real thinking. I don’t believe that. Engagement is still a choice. What we build—and what we choose to care about—remains entirely human.

Day 99

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Birds of a Feather Code Together: Python Find Similar Companies

Day 99 of 100 Days Coding Challenge: Python

Today’s quest: teach Business Rader 3000 how to find “similar companies.” Easy to say, trickier to do. Originally, the plan involved OpenCorporates, Clearbit, and Crunchbase. Yesterday, however, reality crashed the party. OpenCorporates? Pricey. Clearbit? No more free accounts. Crunchbase? Definitely not in the “hobbyist budget.”

At first, I felt like someone standing outside a candy store with empty pockets. But then I realized—who needs the fancy candy shop when you can make your own fudge at home? That’s the fun of coding: it may take more effort (and a little mess), but you can build something that works without spending a fortune. My new strategy? A patchwork of cheaper (or free) sources and a bit of scraping magic. After all, constraints often make the project more interesting.

Today’s Motivation / Challenge

Why does this matter? Because companies don’t exist in isolation. If you know who the competitors are, you suddenly see patterns—like spotting rival coffee shops at every street corner. Finding “lookalike” businesses is the first step toward building a tool that doesn’t just tell you about one company but about the whole ecosystem it lives in.

Purpose of the Code (Object)

The code takes a company name and then searches different sources (Owler, Yahoo, Wikidata) to find competitors or similar businesses. If one source doesn’t answer, another fills the gap. The result is a short list of peers, each with a name and website—kind of like giving you a curated guest list for the company’s business reunion.

AI Prompt

Day 3 of Business Rader 3000
 Find similar companies.

  • Do not use OpenCorporates (too expensive), Clearbit (no more free accounts), or Crunchbase (too expensive).
  • Use Owler for occasional free searches.
  • Use Yahoo Finance for ticker and industry peers.
  • Use Wikidata SPARQL for international coverage.
    Return a list of similar companies + websites.

Functions & Features

  • Prompt user for a company name.
  • Query Owler to get competitors (via scraping or API).
  • Use Yahoo Finance for peer info based on industry/sector.
  • Run a Wikidata SPARQL query to fetch related companies with official websites.
  • Provide a clean list of names + URLs.
  • Normalize company names to avoid simple typos breaking everything.

Requirements / Setup

You’ll need:

  • Python 3.11+
  • Install libraries:

pip install requests yfinance

(Optional: SPARQLWrapper for Wikidata queries.)

Minimal Code Sample

import yfinance as yf

company = “F”  # Ford ticker

info = yf.Ticker(company).info

print(f”Industry: {info.get(‘industry’)}”)

print(“Similar companies: (placeholder for Owler/Wikidata results)”)

This pulls Ford’s industry, then you’d add competitor lookups from Owler or Wikidata.

business-rader-3000

Notes / Lessons Learned

I discovered Owler, which kindly offers free searches if you don’t bombard it. Perfect for this project. Yahoo didn’t have a dedicated “competitors” endpoint, but industry info is enough to infer peers. Wikidata, meanwhile, is like that overachieving student—quirky, international, and full of surprising coverage.

Of course, things went sideways when I mistyped “Ford” as “Forrd.” The script gave me nothing but errors. That’s when I remembered the Japanese concept of poka-yoke: mistake-proofing. I built in a normalization step so my typos wouldn’t torpedo the whole run. I also added a fallback list by industry, so even if the APIs go on strike, I still get something useful back. Debugging felt less like a chore and more like teaching the code to politely say, “Did you mean Ford, not Forrd?”

Optional Ideas for Expansion

  • Add a fuzzy search so the program auto-corrects near-miss spellings.
  • Save competitor lists to a CSV for easy reuse.
  • Visualize the competitor network with a simple graph.

Day 98
Day 100

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When APIs Ghost You: Building a Python Company Industry API Fallback

Day 98 of 100 Days Coding Challenge

Today’s mission: teach Business Rader 3000 how to fetch industry information for a company. Sounds simple, right? Type “Ford Motor Corporation,” press enter, and voilà—get back “automotive industry.” Except, of course, nothing in programming is ever that straightforward.

My first plan was to use Clearbit. Plot twist: Clearbit slammed the door on free accounts. Then I looked at OpenCorporates—yep, that one comes with a price tag too. Fair enough; servers don’t run on fairy dust. But since I’m a hobbyist coder, dropping money on every shiny API is not my jam.

So I went with the Big Three:

  • Yahoo Finance (yfinance) for public companies.
  • Wikidata API for international and quirky cases.
  • Google Knowledge Graph is the “last resort uncle” who knows a little about everything.

Google KG is tricky—you get some queries free, but if you forget to actually activate your account (like I did), you’ll spend hours wondering why nothing works. I had déjà vu from a year ago when I took a Coursera class on Google Cloud and burned through my free credits, then was charged $6.5. It’s possible I may have just expired a trial. To be fair, Google Cloud gave you a way to maintain your costs if you are serious about using their service. That is how I know I spent money. The bright side? Google still gives a generous allowance of daily free queries. It’s like IT giants saying, “Sure, kid, go play in our sandbox—just don’t dig a swimming pool.”

Today’s Motivation / Challenge

Why does this matter? Because knowing what industry a company belongs to is like knowing the genre of a book before you read it. You don’t want to bring a box of tissues to a slapstick comedy, and you don’t want to pitch software to a tire manufacturer. Business Rader 3000 needs this context to do anything useful, and today was the day it learned how to ask smart questions.

Purpose of the Code (Object)

The code takes the company name you provide and then checks three different sources (Yahoo Finance, Wikidata, Google KG) to guess its industry and sector. If one fails, the next one steps up. Think of it as having three friends with wildly different interests—if one doesn’t know the answer, another probably does.

AI Prompt

For your Business Rader 3000 project, I’d suggest:

  • Use Yahoo Finance / yfinance for public companies.
  • Use Wikidata API for broader coverage (private + international).
  • Fall back to the Google Knowledge Graph API for missing entries.

Please add exactly the same functions, but change the source of information.

Functions & Features

  • Prompt the user for a company name.
  • Query Yahoo Finance for industry and sector info.
  • Query the Wikidata API as a second source.
  • Use Google Knowledge Graph as a fallback.
  • Display results in a simple dictionary format.

Requirements / Setup

You’ll need:

  • Python 3.11+
  • Install libraries:

pip install yfinance requests

(Plus a Google API key if you’re using Google KG.)

Minimal Code Sample

import yfinance as yf

company = “F”

info = yf.Ticker(company).info

print({“industry”: info.get(“industry”), “sector”: info.get(“sector”)})

Here we ask Yahoo Finance about Ford, and it politely answers with industry and sector details.

business-rader-3000

Notes / Lessons Learned

When I first ran the program, the response was… silence. No data, no errors, just tumbleweeds. Debugging to the rescue. I added two levels:

  • BR_DEBUG=1 to show which provider answered (or didn’t).
  • BR_DEBUG=2 to print detailed HTTP errors.

That’s when I remembered my weather app fiasco: API keys sometimes need 45–50 minutes to wake up after being created. Turns out, I hadn’t properly activated Google KG at all. Once I fixed that, it worked like a charm. The funny part? By the time I figured this out, I couldn’t tell whether it was my script improving or the API finally letting me in. Either way, I learned something priceless—how to debug like a grown-up.

Optional Ideas for Expansion

  • Save industry results into a local file so you don’t keep hitting the APIs for the same company.
  • Add a fallback “guess” list based on keywords in the company name.
  • Display results in a friendly table instead of a plain dictionary.

        info = yf.Ticker(sym).get_info()

        industry = _normalize(info.get(“industry”))

        sector = _normalize(info.get(“sector”))

        if industry or sector:

            return {“industry”: industry, “sector”: sector or _guess_sector_from_industry(industry)}

Day 97
Day 99

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Building a Python Business Research Tool from Scratch

Day 97 of 100 Days Coding Challenge: Python

Business Radar 3000

The Civilization Timeline project took longer than I wanted. Over the past few days, I had to do a major cleanup because my data suddenly stopped pulling correctly. I spent hours tracking down the cause—chasing bugs like a detective with too much coffee and not enough clues.

That’s when I learned a hard but valuable lesson: always back up your app, no matter how busy you are. If you’re working with environments, GitHub is not optional—it’s survival gear. Unfortunately, this is one of those lessons best learned once… the hard way.

With only four days left in the 100 Days challenge, it’s clear I won’t finish everything neatly by Day 100. Still, instead of panicking, I did what any reasonable person would do: I started a new project.

Our sister company recently introduced an AI-based market analysis app for their business. I wasn’t entirely sure what kind of AI they were using, but it sparked an idea: Maybe I can build something similar myself. The upside? I get to choose exactly which AI to use—and how.

This project also has long-term potential. I can see myself using it as an external analysis tool for my own business in the future, which makes the effort feel far less theoretical.

Knowing this project will likely push me about 7–8 days past the official 100-day mark, I went ahead and launched it anyway.

Thus, Business Radar 3000 was born.

Experience

Today kicked off a brand-new adventure: Business Rader 3000. If that sounds like a sci-fi gadget, well… it kind of is—except instead of spotting UFOs, it’s designed to find businesses. Starting a new project always feels like opening a crisp, blank notebook. You know the one: smooth pages, faint inky smell, the promise that this time, you’ll keep your handwriting neat.

I know this one will run longer than my 100-day countdown—14 days at least. But here’s the twist: I don’t actually care if it spills over. I started this challenge to see if coding would stick. Ninety-seven days in, I already know the answer. Spoiler: it did. I’m not stopping when the counter hits 100.

For setup, I leaned on Poetry again. Last time, I thought I’d forget everything about environments and dependencies, but muscle memory kicked in. Fourteen days may have passed, but the commands still rolled out smoother than I expected. Sure, I peeked at my notes for reassurance, but compared to my earlier flailing, today felt almost professional.

Today’s Motivation / Challenge

Why does this project matter? Imagine a magical Rolodex (remember those?) that not only stores company names but also tells you their industry, who runs them, and what opportunities might be on the horizon. Business Rader 3000 is my attempt at building just that. It’s equal parts detective tool and personal assistant—except this one never complains about coffee breaks.

Purpose of the Code (Object)

The code starts small: it asks for a company name, sets up a safe working environment, and makes sure the right tools are installed. Think of it as laying the foundation for a house—you won’t see fancy wallpaper yet, but without a good base, the whole thing topples.

AI Prompt

Please add the following Tasks.

Day 1 of Business Rader 3000

  1. Prompt the user for a company name.
  2. Set up a virtual environment.
  3. Install necessary libraries.

Functions & Features

  • Ask the user for a company name.
  • Create a clean, isolated Python environment.
  • Install core libraries automatically.

Requirements / Setup

You’ll need:

  • Python 3.11+
  • Poetry (recommended) or pip

If you’re going the pip route:

pip install requests

Minimal Code Sample

# main.py

company = input(“Enter a company name: “)

print(f”Great! We’ll research {company}.”)

One line to ask, one line to confirm. The coding equivalent of saying “hello” before the real conversation starts.

business-radar-3000

Notes / Lessons Learned

Ninety-seven days in, and I’m still tripping over where files should live. Here’s the cheat sheet that finally stuck:

business-rader-3000/ <—- Yes, I have a spelling error, I’ve noticed after I uploaded my app.

├─ .gitignore

├─ .env                 # (later, don’t commit this!)

├─ pyproject.toml       # if using Poetry

├─ requirements.txt     # if using pip

└─ src/

   └─ main.py

Put .gitignore at the root, not buried in some sub-folder. And yes, double-check before pushing to GitHub—nobody wants their API keys doing a world tour. Eventually, this becomes second nature, but while learning, never be shy about asking, “Where does this file go?” It saves hours of detective work later.

Optional Ideas for Expansion

  • Add a greeting that remembers the last company you entered.
  • Store the company name in a text file or database for future sessions.
  • Build a tiny CLI so you don’t have to retype commands every time.

Day 96
Day 98

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Lessons from a Python CSV Seed Import Disaster

Day 96 of 100 Days Coding Challenge: Python

Today was less about writing shiny new features and more about wrestling with files until the app behaved like it did back on Day 19. After nearly four hours of tinkering yesterday, I had to step away—full-time jobs and real life have a way of enforcing time limits. But I was annoyed at myself for one thing: forgetting to commit to GitHub. Again.

This project has grown into a filing labyrinth, the kind where one wrong turn in data/ leaves you staring at errors that make no sense. The safest way to preserve it is to both recreate the file structure and push to GitHub. Of course, I did neither at first. Nineteen days of work, sometimes 4–5 hours at a stretch, left hanging by a thread. Thankfully, I managed to recover everything, lock it with a password, and—finally—commit it to GitHub. Lesson learned.

Today’s Motivation / Challenge

Why does this matter? Because all the code in the world won’t help if your files don’t point to the right place. Data seeds are the backbone of this project: civilizations, events, and sources all live in CSVs. If your app can’t find them, it’s like trying to read a book without the pages.

Purpose of the Code (Object)

The code reconnects the app to its seed files—civilizations.csv, events.csv, and sources.csv. These files feed the database with initial data, ensuring the app has something to display. Without them, the app just spins its wheels and looks confused. By linking everything properly, the app can reload cleanly whenever you restart it.

AI Prompt

We will work on the Import.
Creating the link to the correct data seeds:

  • data/seeds/civilizations.csv
  • data/seeds/events.csv
  • data/seeds/sources.csv

Functions & Features

  • Import civilizations, events, and sources from CSV seed files.
  • Ensure the app database initializes with correct data.
  • Reconnect app logic to seed files if paths are broken.

Requirements / Setup

You’ll need:

  • Python 3.11

Installs:

pip install sqlmodel

Minimal Code Sample

import pandas as pd

civs = pd.read_csv(“data/seeds/civilizations.csv”)

events = pd.read_csv(“data/seeds/events.csv”)

sources = pd.read_csv(“data/seeds/sources.csv”)

Reads the seed CSVs into dataframes for use in the app.

The Civilization Timeline Builder

Notes / Lessons Learned

When you hit a problem importing data, the best thing you can do is stay calm and check filenames line by line. My biggest mistake came from an earlier definition error—I had renamed a file, recreated seeds, and left a trail of confusion. AI didn’t make it worse, but long chats with shifting memory meant old names and new names occasionally got tangled. Programming is like that: fragile, literal, unforgiving.

At one point, I even had the wrong database URL: sqlite:///./data/app.db instead of the correct sqlite:///./data/civ.db. One tiny typo, and the whole app refused to recognize my seeds. Reloading Streamlit without the right database path? A guaranteed disaster.

In the end, a fresh brain helped more than any clever trick. I reconnected the seed files, corrected the database path, and the app sprang back to life. Programming, I’ve realized, is 50% code, 50% precision—and 100% patience.

Optional Ideas for Expansion

  • Add validation scripts to confirm all seed files exist before the app runs.
  • Build a “seed reloader” button in the UI for quick fixes.
  • Let users upload their own seed CSVs to extend the dataset.

Day 95
Day 97

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I Learned How Important it is to Back Up Your files

Day 95 of 100 Days Coding Challenge: Python

Today was supposed to be the grand finale. The plan: add a simple authorization system, set up daily backups, and polish everything with a shiny test suite. Instead, one tiny testing error snowballed into a catastrophic app crash. And the punchline? I hadn’t pushed to GitHub in four days. Yes, four.

So there I was, watching my carefully crafted app crumble, muttering at myself for breaking the golden rule: always back up. The irony stung—after weeks of building a timeline of civilizations that fell for ignoring simple rules, I managed to repeat history myself. Turns out, hubris gets you whether you’re Rome or just a tired coder.

Today’s Motivation / Challenge

Why does this matter? Because by the end of any project, you want to know your work isn’t just duct-taped together. Governance, performance, backups—these are the boring but essential pieces that keep your app from toppling like a badly built tower. Think of it like brushing your teeth: not exciting, but skip it, and you’ll regret it.

Purpose of the Code (Object)

The code here ties up loose ends: adding simple password-based access, creating a daily backup script, optimizing database performance with indexes, and ensuring tests cover at least 80% of the data and API. The goal is to make sure the app runs fast, stays secure, and—most importantly—doesn’t collapse when someone sneezes.

AI Prompt

Please do the following:
Governance & perf

  • Simple auth (env-guarded)
  • Daily backup script
  • DB indexes
  • Test suite

Accept: tests ≥80% coverage on data + API; timeline loads <250ms on sample set.

Functions & Features

  • Add environment-based simple authentication.
  • Run a daily script to back up the database.
  • Add indexes for faster queries.
  • Build a test suite with at least 80% coverage.

Requirements / Setup

You’ll need:

  • Python 3.11

Installs:

pip install pytest

Minimal Code Sample

# Simple env-based auth

import os

PASSWORD = os.getenv(“APP_PASSWORD”)

def check_auth(pw):

    return pw == PASSWORD

A tiny helper to check passwords against environment variables.

The Civilization Timeline Builder

Notes / Lessons Learned

Creating authorization wasn’t the problem—it was forgetting that I’d hardcoded a password in an .env file ages ago and then promptly lost it. Nothing like digging through old notes trying to remember whether you typed password123 or password_123.

Then came the imports. Every time I messed with data/seeds, my app broke in spectacular fashion. I’ve realized most of my headaches came from database imports, not from the app itself. Lesson? When your project leans heavily on a database, really know how you’re connecting to it. Otherwise, you’ll spend more time fixing imports than writing actual features.

In the end, I didn’t get the clean finale I wanted, but I walked away with the most important reminder of all: always, always back up. Civilization may fall without warning, but your repo doesn’t have to.

Optional Ideas for Expansion

  • Add role-based permissions (e.g., read-only vs. admin).
  • Automate cloud backups instead of local scripts.
  • Track performance metrics over time to catch slowdowns early.

Day 94
Day 96

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Packing History in a Zip File

Day 94 of 100 Days Coding Challenge: Python

Today I leveled up my app by teaching it how to bundle everything into one neat little care package: CSV, PNG, quizzes, and all—zipped up and ready to go. Think of it as a take-home kit for history nerds. Instead of juggling screenshots and exports, now you can grab one file, open it later, and relive the glory of your filters and quizzes.

What excites me most is not just the feature itself but the possibilities it unlocks. I suddenly realized this kind of import/export bundle could be handy for any project where you want to ship outputs to others—or just yourself. If I had to re-create the logic from scratch, I’d probably be lost in the weeds. But now? I know the function exists, I know how to call it, and I have the code. That’s enough to carry the trick into future projects. And yes, I’m ridiculously happy about that.

Today’s Motivation / Challenge

Why does this matter? Because nobody wants to lose work. Whether it’s a report, a chart, or a quiz, being able to package and save everything guarantees you can come back to it later. Plus, if you’re sharing your app with others, a zipped curriculum pack makes you look like you thought ahead. It’s like sending someone leftovers in a perfectly labeled container instead of handing them a messy plate.

Purpose of the Code (Object)

The code bundles your selected civilizations, filters, quizzes, and images into a single zip file. Inside, you’ll find JSON, CSV, PNGs, and even a README so you know what’s what. It’s designed to be portable, so you can save a snapshot of your exploration, share it with a friend, or use it as a reference for another project.

AI Prompt

Add the following function:
Curriculum packs

  • Bundle lens + selected civs + quiz + images into a zip.
  • ✅ Accept: zip contains JSON, CSV, PNGs, README.

Functions & Features

  • Collect current filters, data, and quiz results.
  • Export charts and timelines as PNGs.
  • Save everything into a single zip archive.
  • Include a README file for clarity.

Requirements / Setup

You’ll need:

  • Python 3.11

Installs:

pip install plotly kaleido streamlit

Minimal Code Sample

import zipfile, io

with zipfile.ZipFile(“curriculum_pack.zip”, “w”) as zf:

    zf.writestr(“filters.json”, json.dumps(filters))

    zf.writestr(“data.csv”, df.to_csv(index=False))

    zf.writestr(“README.txt”, “Curriculum pack export”)

Writes JSON, CSV, and a README into a zip file in memory or on disk.

The Civilization Timeline Builder

Notes / Lessons Learned

Sometimes it’s the little discoveries that bring the most joy. I used to write imports like:

import io

import csv

But today I learned you can just write:

import io, csv

It’s small, but it makes me smile—one of those tiny conveniences you only stumble upon while coding.

The helper function itself starts by converting Plotly figures into PNG bytes (thanks to Kaleido, which I’d already added earlier). Then, with the io library, I created in-memory streams for text and files. io.StringIO in particular is handy: it treats strings like files, making them easy to read and write. I’d used it before for downloads, but seeing it in action again for zipping files reminded me just how versatile it is.

The result: a neat little archive of history in my downloads folder. Simple, but powerful.

Optional Ideas for Expansion

  • Add an “unzip and load” feature so packs can be re-imported into the app.
  • Include a summary.txt file with auto-generated narrative recaps.
  • Allow multiple packs to be batched into one archive for classroom use.

Day 93
Day 95